Podcast

Podcast

IBM: View from the cutting edge of AI

If you ask her about emerging technologies, Sophie Vandebroek – VP of Emerging Technology Partnerships at IBM – can tell you firsthand how the pace of change is moving faster and faster. From AI to blockchain, Bill talks with Sophie about how businesses are harnessing the cutting edge of advanced technologies and what one of the world’s largest enterprise technology companies has learned from putting them to use. Link to transcript.

Bill Kerr: The ongoing revolution in technology is frequently compared to the enormous transformations that accompanied steam power and electricity. From artificial intelligence to quantum computing, daily headlines describe the new strides being made. Yet it’s often hard to separate reality from hype, much less to understand how best to prepare.

Welcome to the Managing the Future of Work podcast from Harvard Business School. I’m your host, Bill Kerr. Today, I’m speaking with Sophie Vandebroek, vice president of emerging technology partnerships at IBM. Sophie’s exceptional career has also included being the chief operating officer of IBM Research and the chief technology officer at Xerox. Sophie will describe the major inflection points ahead in technology and the challenges leaders will face in managing them. Welcome, Sophie. Thank you for joining us.

Sophie Vandebroek: Oh, it’s a great pleasure to be here, Bill. Thanks for having me.

Kerr: Sophie, tell us a little bit about your current role at IBM.

Vandebroek: So, in my current role, I have the privilege to work with brilliant researchers around the globe, as well as clients and partners, in really identifying what are the key challenges, what are the key opportunities, and then jointly making them a reality.

I was actually born and raised in Belgium, came to the US for graduate school, and my first job after getting my PhD in microelectronics was at IBM Research. Unfortunately, I lived like seven hours from work, and so that was not possible after the second kid was born, and I joined Xerox Research up in Rochester, New York, where I also had the privilege of working with many great researchers around the globe and with our clients. For the last 11 years, as you mentioned, I was chief technology officer. And then, when Xerox split into two companies, both Xerox and Conduent, I decided to go back to IBM Research because, indeed, IBM has the best commercial research lab in the world. We have several Nobel Prize winners and fellows in their societies, their technical societies. We are working on technologies—everything from nanotechnology to microelectronics, cloud security, Internet of Things, quantum computing …

Kerr: Yeah, we’ve got a lot to cover in this podcast!

Vandebroek: … AI, and everything in between. There are almost 400,000 IBMers that can then help solve these innovations, work lines, and make an impact on billions of people.

Kerr: So, artificial intelligence is currently a hot topic. Tell us a bit about the technology development side. What has caused this?

Vandebroek: We are at the beginning of an exponential curve, I would say, of accelerating new knowledge creation. But, in fact, artificial intelligence, itself, has been around for decades. Let me describe it a little bit.

In 1955, Marvin Minsky and an IBM manager, Mr. [Nathaniel] Rochester, together with two other co-authors drafted a proposal to hold a summer workshop at Dartmouth on artificial intelligence. And it’s the first document in which the word “artificial intelligence” was coined. In fact, if you go read it, they talk about neural nets and about learning and actual language. And so they also created the initial neural network deep-learning algorithms. However, it didn’t go very far, because there was no data to train these neural networks, and neither was there computing power to really do the training with all the data.

And so, fast forward, thanks to Moore’s Law, there is now huge computing power available. The transistor was invented in the ’50s, 1,000 transistors per square centimeter in the ’70s, and today 10 billion transistors on the same chip, right? So huge high-performance computers are available in the cloud for training your neural nets, as well as, of course, computing power led to amazing computing capabilities in all of our pockets.

That combined with what’s called Metcalfe’s Law—which is Bob Metcalfe. He was a Xerox partner when he invented the ethernet, which led to the internet, which led to the networking effect when all the devices and laptops and computers were connected—the power of the network is proportional to the number of users on the network, and that’s called Metcalfe’s Law. It’s actually 2n [two to the nth]. But that network effect has led to some of the most valuable companies in the world today, and it has also led to an explosion of data. I mean, lots of digital data is available today.

And so the combination of the digital data being available and the computing power is such that, at this moment in time, AI is real. So these neural networks, these deep-learning algorithms for the first time back in 2012, they were better than the handcrafted algorithms that were there before, or even better than humans at categorizing, for example, images. But it was a combination of computing power and data that really makes AI a reality.

Kerr: The concept has been around for a long time, we just didn’t have the right ingredients to put in there.

Vandebroek: Yeah. We didn’t have the right infrastructure, the right “ingredients,” as you say, between the data and the computing power to make AI a reality. We’re at the beginning of this exponential curve.

All of us are used to living in linear time, right? Distance is linear. Time is linear. Exponential is like driving a car that’s constantly accelerating. It’s going to go very, very fast. So all industries—everybody—needs to understand, “What is AI going to do to my business?”

Kerr: Yeah. One of the ways I phrased it yesterday was that we were having the election. Yesterday was the midterm elections. Between then and when we have the next election, the presidential election, there’ll be the two years. And so, if computing power is doubling every two years, that means what we have then will be the equivalent to everything that’s come up until this midterm election. What are the new capabilities that are emerging with this power?

Vandebroek: So, some have already emerged. Today, artificial intelligence, and especially what’s called “narrow AI,” is very good. Let me first explain narrow AI—and we just exited the narrow AI phase—is able to do things at superhuman capability. But it’s very limited to what’s possible. For example, it’s able to recognize skin cancer or to cluster and then categorize certain documents. Like in health care payments, the health care forms and documents that the doctors submit to get reimbursed, most of that is handled and recognized with AI. Natural language processing, extracting the content out of the forms, knowing what kind of forms it is, and then automating the backend processes.

So today, AI is doing a lot of things, but they’re very well-defined tasks. I mean, many people might be familiar with virtual agents that help people book a flight or that help you service or figure out problems that your mobile phone might have. But again, it’s very narrow problems.

Kerr: Yeah, then you get frustrated and ask for a real agent.

Vandebroek: Yeah. In some cases, you get frustrated, because it’s narrow, right? If it has something it’s never seen before, it can’t answer you. And so, what AI can do today very well—especially with visual images—is looking at images and being able to categorize or cluster new concepts. It’s able to then use automated processes. It’s able to do simple predictions. In the future, what we see is that artificial intelligence will also be able to create new knowledge and to accelerate discovery and to help people be more creative and then go from there to become ultimately general AI, which is a computer that can do and think and act very similar to humans. But the belief and the point-of-view by experts today is that’s going to be 2050 and beyond.

Kerr: Okay, for the general AI.

Vandebroek: By the time we are—at least I am—very old.

Kerr: I think we all age at the same rate.

Vandebroek: That’s right.

Kerr: Althought sometimes I feel I’m aging exponentially, but let’s not come back to that. What are some things IBM is doing?

Vandebroek: We have worked with many health care and life sciences enterprises. For example, we work with Pfizer to be able to look and observe in a non-exclusive way—leveraging cameras in a house, for example—on how individuals with Parkinson’s disease, how the medicine is working—I mean, whether they are moving normally or whether the medicine is not working and they have some issues. And so, with visual analytics, you can analyze the impact of medicine.

In addition, if you actually listen to patients—and in the same house on our campus and the research headquarters in Yorktown Heights, we have installed microphones to actually listen to people and listen to how they talk, and then we can analyze the speech patterns. And based on analyzing and looking at the speech patterns, you can, in fact, learn a lot about the mental health of an individual. And so, working with the National Institute of Health and training our algorithms on patients that we know have schizophrenia or mania or that have the onset of Alzheimer’s, we have trained them on a couple of hundred patients. But then, looking at new patients, you can, leveraging these algorithms, predict whether they have certain mental health situations. That is very valuable, because today more than 5 percent of individuals have mental health issues. It’s a big cost. It’s a slow process to be able to diagnose. So if you can make it more accessible to more individuals, and more broadly, you can have a positive impact on health.

Giving an example that’s more business related, like what we do at IBM ourselves today, is we are using deep-learning algorithms to help the salespeople better create bids or better create pricing when we bid on an outstanding solution that we’re going to provide to a client, right? So at IBM, every individual solution that’s provided is different. They’re all unique. It’s not just a price list, I mean, that A costs so much, et cetera. Every solution is unique, so it has certain software, it might have some consulting associated with it, it has some after-sale maintenance.

Kerr: It’s very differentiated, both from competitors and also from the other things you’ve done …

Vandebroek: … from other services we provide to different clients. So it is not a standard price. And so, in the past, salespeople would, based on their experience, make up a price for what they are selling to this particular enterprise.

Overall, I mean, efficiency has gone from four hours to zero. It’s instant. You put it in, and the algorithm says, “Okay, as long as you stay within this boundary, you can go forward with your customer, and the revenue is increased by 8 percent.” And if you sent 50,000 different bids through this system, and every month we retrain … one of things you have to do when you deploy these deep-learning neural network space systems is you have to retrain them about every month or every quarter with the new data as it comes in.

Kerr: Okay, because the world changes.

Vandebroek: Because the world changes, or you have new information available. So, it’s a constant learning, right? Systems continue to learn. It’s not like you program them once and that’s it. It’s like, as things change, the systems have to continue to learn.

Kerr: What gets in the way of more-rapid adoption of some of these technologies?

Vandebroek: I mean, there’s several challenges. Let me list a few in order. Number one is being able to have clean data that’s accessible. Often, if you look at an enterprise, and even different businesses within the enterprise, the data is in different locations. It might reside both on premise or within the company itself or in multiple clouds. It’s really hard to get to the data, and often it’s unstructured—80 percent of the data is unstructured information. It’s not just nice numbers in databases. And often the data is also noisy. And so being able to have clean, structured data that you can use within algorithms is a first challenge. And for example, the acquisition that IBM is working on with Red Hat, who has also been our partner for many years …

Kerr: … a very big acquisition.

Vandebroek: Oh, yeah. It will allow clients to more easily access their data across multi-clouds and on prime hybrid-cloud environments and be able to then use the data across these different clouds to actually create insights leveraging AI algorithms, et cetera. So the collecting, cleaning, the data is a very big one.

Number two is in the deep, narrow AI—which is also what’s been used by consumers on the internet. It’s things that were trained, algorithms that were trained, on millions of images. For example, algorithms easily recognize a cat is a cat, or other objects, but has been trained on really huge volumes of images. In an enterprise setting—whether you’re a hospital, or a school, or a small or medium business, you don’t have millions of data points to train on. A hospital might have several thousands of patients, but maybe, if you’re looking at a certain, specific disease, maybe only a couple of hundred that have the disease. So it’s just training on small data, which is a big challenge.

And that’s where research is happening, both at IBM Research—as well as, for example, in MIT-IBM Watson AI Lab—on what’s called “transfer learning,” that you can train AI models on one set of data, and then personalize it, or customize it, for giving to hospitals with their own data. Because one of the core principles of IBM, for example, is that our clients’ data is their data; their insights are their insights. So we never use client A’s data to train neural networks then used with client B. We really keep that data separate. So being able to do transfer learning and being able to train based on small data sets.

Kerr: And how far are we down that journey? Conceptually, technically, how far are we down that journey to being able to do AI on small data sets?

Vandebroek: There is good progress being made. For example, we work with clients on being able to train algorithms that can be creative. So an algorithm—and this is, for example, with Symrise, this one company where we have trained algorithms to create new perfumes, new fragrances—and so you can train it to create perfumes, and, in fact, we did it, to create perfumes for millennials in Brazil for one of the companies that they were providing the perfume to. And let me first talk a little bit about that algorithm. The algorithm, itself, is not limited by what the perfumer knows. Normally, it takes like 10 years to train a perfumer. There are trillions of possibilities of putting new combinations together of 100,000 formulas and thousands of ingredients. And so what we did is we trained this algorithm with all this past data that the company had, to where we knew the outcome: This was a success, this was not a success. You know the price outcome, you know, also, the ingredients. And so then, if new customers show up, you can much more easily create, or at least a new starting formula, from which you were to create a new fragrance.

Everything, the whole cycle, is much faster. It can be lower cost. Now, if the company, once these neural networks are trained, and instead of perfumes, you want to do flavors, for example, you could potentially retrain similar algorithms with flavors and then come up with new flavors.

I think the next one is transparency. In the age of narrow AI, many of these algorithms will give you an answer. It will tell you, if you apply for a loan, the algorithm might say yes or no, you get a loan. But it doesn’t tell you why they came up with the answer. So it’s like black boxes. And that is also not acceptable for enterprises. The clients are going to want to know, “Why do I have cancer?” and “Why I am I rejected for this loan?” or “Why am I not hired?” Not just, yeah, the algorithm thought your resume wasn’t good enough.

The other one is the trust, and to make sure that the algorithms are fair and not biased. There are also many examples of algorithms that were used, for example, in hiring. You’re going to hire a software engineer for your company, and most of the resumes you will get are all, for example, resumes from men, and not from women. And, of course, the issue there is that the whole training set, the decades of training, the type of people you’ve hired have naturally been more men than women. But you don’t want the algorithm to rule out exceptional women resumes because they are a woman. The algorithm, you can get into this vicious circle of reinforcing bias. So being sure that the algorithm is unbiased, whether it’s from gender, or race, or age, or where you live, and many other elements of bias.

I mean, this ongoing research is very important, and, in fact, at IBM, we have recently open-sourced AI Fairness 360 ethics toolkits, where you can bring your models that you have trained, no matter which framework you use to train your model. You bring it to the—it’s called “AI Open Scale.” It builds on the AI studio that’s on the IBM cloud. But you bring in your model, and the system will look at your model and decide if there is bias or not, and then give recommendations on how to fix the bias.

Kerr: Let’s talk about another big word that’s out there, which is “blockchain.” Can you tell us a little bit about what IBM is doing in this space?

Vandebroek: Yeah. We are actually doing a lot. People think blockchain, they often think bitcoin or cyber currencies.

Kerr: Yeah, exactly. There’s a lot of, obviously, big swings in those prices and questions about what’s hype or what’s going to be real about that.

Vandebroek: Yeah. And so, in IBM, we are not interested in creating cyber currencies. Everything we do is built on the Hyperledger, which is also an open-sourced platform. And, in fact, a lot of the code in Hyperledger came from IBM, but many other companies and developers are contributing. We have now a coin that is also part of the blockchain solution but that’s linked to the fiat currency, so there’s the dollar, and the euro, and the yen. Because people do have monetary transaction needs on the blockchain, but it’s not speculative, and it’s related to being able to do transactions. Because fiats, there are kind of like three categories that blockchain is being used for now. One is these crypto currencies, and I’m not going to talk about that because most enterprises are not interested in that. Number two is to create trust in physical goods, or high-value digital goods. Or are shipped and transacted across entities that might not normally trust each other. And so it’s all around the value chain. I will give a couple of examples there. But the third one is around digital identity and authentication of whether it’s small or medium businesses, or even, in the future, individuals as a digital self.

But let me give some example of the value-chain project. So, one is we worked initially in research, and then the business unit with Walmart as food was shipped. Like, we started the project with pork, I believe, from a farmer in China to the table, and making sure that it was understood, all of the transactions that happened in this process. And they were focused on food safety, that was the initial goal for Walmart. And in the meantime, over the next several years, the Walmart food safety network has broadened, and now most of Walmart’s suppliers are in the network. So, as you can see, it’s a permission-based network. So Walmart is there, many suppliers, and many other companies, and many stores. Everybody’s in the network, but it’s permission based. It’s not open to just anybody to come in. The ledger of all the transactions is distributed and visible to everybody in the network, and every time a transaction happens, like from a farmer to a distribution center, or to a store, or any of these transitions create a new block in the blockchain that has all the content of what actually happened. It’s encrypted. It’s immutable. Nobody can change it without everybody else seeing it. And by doing that—now when there is, for example, an outbreak of poisoning … not poisoning, but like E. coli or illness, foodborne illness …

Kerr: Exactly. We have fast-food restaurants or other ...

Vandebroek: … yes, which really makes about 48 million people in US sick every year. It’s a huge number. And it’s a huge cost. About $60 billion in food is destroyed. That’s all pre-blockchain infrastructure. Now with the blockchain, if there is an E. coli outbreak, for example, you can almost instantaneously track back, “Where did this exact lettuce come from? Or where did this exact produce come from?” You can track it all the way back to the farm. So instead of needing to take so much food off the shelves in the store, and a whole value chain being shut down for seven weeks because it’s just so hard to track it down, it’s like instant. So that’s very powerful for Walmart and others that are in the food industry.

Kerr: There’s a powerful future there. And I want to also ask you for a few minutes to talk about quantum computing, which we hear a lot in the headlines right now as well. Can you tell us just a little bit about what this is doing? Is it really going to wreck all of our cybersecurity sort of passwords and protections? And when is this going to have an influence in all of our lives?

Vandebroek: Yes. So again, quantum computing is one of those technologies that’s on an exponential curve. It was back in the 1980s, actually 1981, that Richard Feynman, at a conference also organized by IBM and MIT, coined the term “quantum computing,” because the notion is nature isn’t digital, so it based more like quantum physics. So can we figure out a computer that computes more like in the quantum physical way. In digital computing is very, very fast right now. In fact, just a few months ago, the latest high-performance computer, called “Summit,” was sold by IBM to the National Lab in the US, and it has 200 petaflops, or 200,000 trillion calculations, per second.

Kerr: Sounds like a lot.

Vandebroek: That is a lot. That’s super fast. But, nevertheless, these high-performance computers, based on these digital transistors as the basic mechanism, can’t even simulate molecules that are kind of like a caffeine molecule. I mean, at a very accurate level, all the energy levels, et cetera, within those molecules, those chemical molecules. And so, if you could get a quantum computer to work, where the unit itself, a transistor, is a cubit, and this cubit has two characteristics. So instead of just being a one or a zero, they can be an enormous amount of states at the same time, and they are entangled. One cubit is entangled to all of its neighbors, and so these two characteristics make it such that these computers can do an enormous of amount parallel computing power. IBM had a five-cubit machine about a year and a half ago; then we announced roughly a 20-cubit machine about a year later; now we’re up to a 50-cubit machine, which is very large. We have open sourced ... IBM Q it’s called, a quantum computer. It’s in the lab, but you can access it through the internet. And, in fact, many researchers have accessed the computer, about 70,000 people around the globe have run programs on this computer. Everything from just simply playing with the cubits to creating simple games. Six million experiments have been done, and it just keeps rising. So there is a huge amount of interest, not only ... and 120 scientific papers already have been written. In fact, we had the cover of Nature magazine simulating simple molecules, which showed that a quantum computer can, indeed, do that much better than digital computers.

But, yes, what you said is, what the scientists predict is that this quantum computer can, in fact, break cybersecurity encryption methodologies that are in use today. Luckily, the quantum computer ... Our point of view is that this won’t happen until there are thousands of cubits. Today we have 50, so it’s not tomorrow.

Kerr: But you’re growing rapidly.

Vandebroek: But yes, in this exponential world, and growing fairly rapidly, that is definitely something we predict will happen, right? And so, in parallel, security researchers are working on methodologies like n-dimensional lattice encryption which will be way harder to break, even by quantum computers.

Kerr: When do you think quantum computing is going to impact the average person’s life in a substantial way? Is that five years out? Ten years out?

Vandebroek: I think it’s very hard to predict, because the technology’s so hard. Yes, we have 50 cubits. In order to truly impact our lives, you need to get at least to 5,000 or more, right? So I would say three, five years will be ... But before that, clearly, life sciences organizations.

Kerr: There’ll be certain very clear research applications.

Vandebroek: ... financial institutions, security. I mean ... and commercial applications to start developing. You will be able to accelerate material discovery if you can clearly simulate these molecules, right? But then, between coming up with a new discovery for life sciences and it impacting me as an individual, there is still a lead time, right?

Kerr: Yeah.

Vandebroek: FDA approval, all the other things. So I would say, if you go play with it today, it can impact you today. I would clearly encourage people to learn, not only about quantum, but about blockchain and AI. There are lot of open courses. In fact, IBM has worked on edX, it’s Harvard, MIT ... movie theater they call it. There are now courses on open source, on quantum computing, on blockchain, there are courses on AI.

Kerr: Many places to go.

Vandebroek: Yeah, many place to go and learn about these technologies and think about them.

Kerr: So, Sophie, you’ve been a strong advocate for women in STEM fields, and also talked about your personal life as a working mother with a demanding career. Tell us a little bit about your perspectives here, and some advice you might give.

Vandebroek: Yes, indeed. Throughout my career I had three kids, which are now all—knock wood—happy, mature adults, which is fantastic. —there are many pieces of advice, right? Number one, make sure you work for a company where you can truly be yourself, right? Where you can bring yourself to work, where you don’t need to wear a suit that doesn’t fit. And so, no matter who you are, right? It could be whether you’re a man or woman, whether you’re gay, lesbian, bisexual, transgender, whether you’re religious or atheist, whether you’re old or young, whether you’re physically able or not. So finding an organization where you can truly be yourself is very important. In addition, working on something ... Because then, for example, you can’t pick up the phone if your kids calls, and it’s not going to be looked at as, “Why is her kid calling during the meeting?” for example, right? So somewhere where you can bring your full self, which includes your family, to work. And also work for a company where you’re really passionate about what you’re doing. Life is just way too short, and you’re going to be less happy if you don’t fully care about what you’re working on. It could be the purpose of the company, it could be ... for many scientists, it’s being challenged by solving a difficult problem. It could be you like your colleagues, it’s just …

Kerr: Yeah, there’s the connection there.

Vandebroek: ... fun working, which is very, very important. But then, specifically for individuals, or women, or men, is ... Look at it in three areas. Number one is being able to prioritize. You can’t do everything, right? In my case, I had three little kids, I was raising them alone, and I had my work. and they vary as your life goes on, right? So, in my case, my priorities were my kids and my work, and not as much ... Like, I didn’t organize many parties, or had a lot of time to go out for coffee with friends, or be involved in volunteering. There was just no time. Time is always so limited, so being able to prioritize is very important. And for everybody, that’s different. But again, for me, it was my kids and then my work. And I also realized very quickly, that unless you also prioritize your own health, your own well-being, forget about everything else, right? So prioritize being able to have enough hours of sleep, eating healthy, exercising, and always making sure I had at least one good friend, right? I mean so you have to prioritize. And then, once you prioritize, you lean in on those things that you care about, right? You lean in on making sure you’re there for the right events in your kids’ school, or lean in at work, that you write and publish papers in the best journals, or that you really accomplish your project and exceed your manager’s expectation. And so, you really lean in in the couple of areas that you selected, right? But more important, and that’s where it’s hard for many people, you lean out of everything else.

Kerr: Okay. Tell us a little about leaning out.

Vandebroek: For example …

Kerr: … I think you have a great book title.

Vandebroek: Yeah, Leaning Out. Right? Exactly. In my case, one of the things was, I hired a student to go grocery shopping for me. And you don’t have to be rich to do that, because, in fact, if every time I went grocery shopping, I would add some good European cheese, or some olives, and, boom! It was already more expensive than having a student go shopping for an hour or a couple of hours, and just following exactly the spreadsheet of whatever was checked off once a week. And so, yes, you then have to accept that the bananas might be more ripe than you would have selected them, or more green. So you have to kind of live with some of the trade-offs. But at least these kind of things, a lot of the jobs and what women do today, and men, can be basically outsourced. And again, it doesn’t need to be expensive. And, in fact, today Amazon shows up and puts all of that on my doorstep. It’s even easier than that. We’re talking 20 years ago. But you get the concept, right? Being able to just lean out. Saying “no” to many things, taking simple vacations that don’t require a lot of planning, et cetera. So that is just very important: Try to prioritize. Then the leaning in, because then you’re going to feel good about yourself, and your kids, and your work, if you really commit a significant amount of time on that, and leaning out as much as possible on everything else.

Kerr: Let me ask one final question, which is ... Any parting advice for MBA graduates, given the dynamic future ahead, how they should manage that?

Vandebroek: Yeah, definitely. One is, since we discussed artificial intelligence a lot, right? And artificial intelligence is really the new electricity, it’s the new internet. Make sure you take a course before you graduate, and understand the basics and see how you can infuse these technologies in whatever future job you’re going to take. But not only that; my major piece of advice is continue to always learn, right? Always be a student. Five years, 10 years, there will be new technologies. I mean, constantly learn, because the world doesn’t stand still, and so you can’t just say, “I’m finished learning. Now I go to work.” No, you have to be a student your whole life.

Kerr: Great. Thank you, Sophie, for walking us through the technology roadmap ahead, and the big issues on the horizon.